This paper compares the performances of a number of uniform circular array (UCA) configurations for phased array antennas. A UCA geometry is targeted due to its symmetrical configuration which enables the phased array antenna to scan azimuthally with minimal changes in its beam width and sidelobe levels. Each UCA configuration consists of 19 isotropic elements. Particle Swarm Optimization (PSO) is used to calculate the complex weights of the antenna array elements in order to adapt the antenna to the changing environments. Comparisons are made in the context of adaptive beamforming properties and Signal to Interference Ratio (SIR). The results obtained suggest that a planar uniform hexagonal array PUHA (1:6:12) is suitable for high resolution applications as its sidelobe levels are the lowest compared to the other geometries.

Remote sensing has emerged as a promising tool for monitoring water quality (WQ) in aquatic ecosystems. This study evaluates the effectiveness of remote sensing in assessing WQ parameters in Cajititlán and Zapotlán lakes in the state of Jalisco, Mexico. Over time, these lakes have witnessed a significant decline in WQ, necessitating the adoption of advanced monitoring techniques. In this research, satellite-based remote sensing data were combined with ground-based measurements from the National Water Quality Monitoring Network of Mexico (RNMCA). These data sources were harnessed to train and evaluate the performance of six distinct categories of machine learning (ML) algorithms aimed at estimating WQ parameters with active spectral signals, including chlorophyll-a (Chl-a), turbidity, and total suspended solids (TSS). Various limitations were encountered during the study, primarily due to atmospheric conditions and cloud cover. These challenges affected both the quality and quantity of the data. However, these limitations were overcome through rigorous data preprocessing, the application of ML techniques designed for data-scarce scenarios, and extensive hyperparameter tuning. The superlearner algorithm (SLA), which leverages a combination of individual algorithms, and the multilayer perceptron (MLP), capable of handling complex and non-linear problems, outperformed others in terms of predictive accuracy. Notably, in Lake Cajititlán, these models provided the most accurate predictions for turbidity (r² = 0.82, RMSE = 9.93 NTU, MAE = 7.69 NTU), Chl-a (r² = 0.60, RMSE = 48.06 mg/m³, MAE = 37.98 mg/m³), and TSS (r² = 0.68, RMSE = 13.42 mg/L, MAE = 10.36 mg/L) when using radiometric data from Landsat-8. In Lake Zapotlán, better predictive performance was observed for turbidity (r² = 0.75, RMSE = 2.05 NTU, MAE = 1.10 NTU) and Chl-a (r² = 0.71, RMSE = 6.16 mg/m³, MAE = 4.97 mg/m³) with Landsat-8 radiometric data, while TSS (r² = 0.72, RMSE = 2.71 mg/L, MAE = 2.12 mg/L) improved when Sentinel-2 data were employed. While r² values indicate that the models do not exhibit a perfect fit, those approaching unity suggest that the predictor variables offer valuable insights into the corresponding responses. Moreover, the model’s robustness could be enhanced by increasing the quantity and quality of input variables. Consequently, remote sensing emerges as a valuable tool to support the objectives of WQ monitoring systems.

In this paper, electrocoagulation (EC) treatment for the removal of chemical oxygen demand (COD) from cold meat industry wastewater is modeled and optimized using computational techniques. Methods such as artificial neural networks (ANNs) and response surface methodology (RSM), based on the Box–Behnken design using three levels, were employed to calculate the best control parameters for pH (5–9), current density (2–6 mA/cm²) and EC time (20–60 min). Analysis of variance (ANOVA) and 3D graphs revealed that pH and current density are the main parameters used for depicting the EC process. The developed models successfully describe the process with a correlation coefficient of R² = 0.96 for RSM and R² = 0.99 for ANN. The models obtained were optimized applying the moth-flame optimization (MFO) algorithm to find the best operating conditions for COD removal. ANN-MFO was used and showed superior COD removal (92.91%) under conditions of pH = 8.9, current density = 6.6 mA/cm² and an EC time of 38.62 min. The energy consumption with these optimal conditions was 6.92 kWh/m³, with an operational cost of $3.14 (USD)/m³. These results suggest that the proposed computational model can be used to obtain more effective and economical treatments for this type of effluent.

In this paper, an optimal radiation pattern is obtained for a linear antenna array using the particle swarm optimization technique. A set of phase shift weights are generated in order to steer the beam towards any desired direction while keeping nulls in the direction of interferers. The fitness function that allows the calculations of the phase shift weights is presented. A comparison between the standard genetic algorithm and the particle swarm optimization was studied and the results show that the latter achieves a better and more consistent radiation pattern than the GA. Moreover, a number of experiments show that the PSO is capable of solving the problem using less number of fitness function evaluations in average.

In several machine vision problems, a relevant issue is the estimation of homographies between two different perspectives that hold an extensive set of abnormal data. A method to find such estimation is the random sampling consensus (RANSAC); in this, the goal is to maximize the number of matching points given a permissible error (Pe), according to a candidate model. However, those objectives are in conflict: a low Pe value increases the accuracy of the model but degrades its generalization ability that refers to the number of matching points that tolerate noisy data, whereas a high Pe value improves the noise tolerance of the model but adversely drives the process to false detections. This work considers the estimation process as a multiobjective optimization problem that seeks to maximize the number of matching points whereas Pe is simultaneously minimized. In order to solve the multiobjective formulation, two different evolutionary algorithms have been explored: the Nondominated Sorting Genetic Algorithm II (NSGA-II) and the Nondominated Sorting Differential Evolution (NSDE). Results considering acknowledged quality measures among original and transformed images over a well-known image benchmark show superior performance of the proposal than Random Sample Consensus algorithm.